Classification
Identifying which category an object belongs to.
Applications: Spam detection, image recognition.Algorithms:Gradient boosting,nearest neighbors,random forest,logistic regression, andmore...
Regression
Predicting a continuous-valued attribute associated with an object.
Applications: Drug response, stock prices.Algorithms:Gradient boosting,nearest neighbors,random forest,ridge, andmore...
Clustering
Automatic grouping of similar objects into sets.
Applications: Customer segmentation, grouping experiment outcomes.Algorithms:k-Means,HDBSCAN,hierarchical clustering, andmore...
Dimensionality reduction
Reducing the number of random variables to consider.
Applications: Visualization, increased efficiency.Algorithms:PCA,feature selection,non-negative matrix factorization, andmore...
Model selection
Comparing, validating and choosing parameters and models.
Applications: Improved accuracy via parameter tuning.Algorithms:Grid search,cross validation,metrics, andmore...
Preprocessing
Feature extraction and normalization.
Applications: Transforming input data such as text for use with machine learning algorithms.Algorithms:Preprocessing,feature extraction, andmore...
"We use scikit-learn to support leading-edge basic research [...]"
"I think it's the most well-designed ML package I've seen so far."
"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]"
"The great benefit of scikit-learn is its fast learning curve [...]"
"It allows us to do AWesome stuff we would not otherwise accomplish."
"scikit-learn makes doing advanced analysis in Python accessible to anyone."
